Time series saliency maps: explaining models across multiple domains

• URL: http://arxiv.org/abs/2505.13100v1
• Date: Mon, 19 May 2025 13:31:35 GMT
• Title: Time series saliency maps: explaining models across multiple domains
• Authors: Christodoulos Kechris, Jonathan Dan, David Atienza,
• Abstract summary: We introduce Cross-domain Integrated Gradients, a generalization of Integrated Gradients.<n>Our method enables feature attributions on any domain that can be formulated as an invertible, differentiable transformation of the time domain.<n>These results demonstrate the ability of cross-domain integrated gradients to provide semantically meaningful insights in time-series models.
• Score: 3.9018723423306003
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Traditional saliency map methods, popularized in computer vision, highlight individual points (pixels) of the input that contribute the most to the model's output. However, in time-series they offer limited insights as semantically meaningful features are often found in other domains. We introduce Cross-domain Integrated Gradients, a generalization of Integrated Gradients. Our method enables feature attributions on any domain that can be formulated as an invertible, differentiable transformation of the time domain. Crucially, our derivation extends the original Integrated Gradients into the complex domain, enabling frequency-based attributions. We provide the necessary theoretical guarantees, namely, path independence and completeness. Our approach reveals interpretable, problem-specific attributions that time-domain methods cannot capture, on three real-world tasks: wearable sensor heart rate extraction, electroencephalography-based seizure detection, and zero-shot time-series forecasting. We release an open-source Tensorflow/PyTorch library to enable plug-and-play cross-domain explainability for time-series models. These results demonstrate the ability of cross-domain integrated gradients to provide semantically meaningful insights in time-series models that are impossible with traditional time-domain saliency.

Related papers

• Cross-Domain Conditional Diffusion Models for Time Series Imputation [34.400748070956006]
  Cross-domain time series imputation is an underexplored data-centric research task.<n>Existing approaches primarily focus on the single-domain setting.<n>Our proposed solution integrates shared spectral components from both domains while retaining domain-specific temporal structures.
  arXiv  Detail & Related papers  (2025-06-14T09:09:07Z)
• Multivariate Long-term Time Series Forecasting with Fourier Neural Filter [55.09326865401653]
  We introduce FNF as the backbone and DBD as architecture to provide excellent learning capabilities and optimal learning pathways for spatial-temporal modeling.<n>We show that FNF unifies local time-domain and global frequency-domain information processing within a single backbone that extends naturally to spatial modeling.
  arXiv  Detail & Related papers  (2025-06-10T18:40:20Z)
• Learning Latent Spaces for Domain Generalization in Time Series Forecasting [60.29403194508811]
  Time series forecasting is vital in many real-world applications, yet developing models that generalize well on unseen relevant domains remains underexplored.<n>We propose a framework for domain generalization in time series forecasting by mining the latent factors that govern temporal dependencies across domains.<n>Our approach uses a decomposition-based architecture with a new Conditional $beta$-Variational Autoencoder (VAE), wherein time series data is first decomposed into trend-cyclical and seasonal components.
  arXiv  Detail & Related papers  (2024-12-15T12:41:53Z)
• Towards Generalisable Time Series Understanding Across Domains [10.350643783811174]
  We introduce a novel pre-training paradigm specifically designed to handle time series heterogeneity.<n>We propose a tokeniser with learnable domain signatures, a dual masking strategy, and a normalised cross-correlation loss.<n>Our code and pre-trained weights are available at https://www.oetu.com/oetu/otis.
  arXiv  Detail & Related papers  (2024-10-09T17:09:30Z)
• Memory-Efficient Prompt Tuning for Incremental Histopathology Classification [69.46798702300042]
  We present a memory-efficient prompt tuning framework to cultivate model generalization potential in economical memory cost. We have extensively evaluated our framework with two histopathology tasks, i.e., breast cancer metastasis classification and epithelium-stroma tissue classification.
  arXiv  Detail & Related papers  (2024-01-22T03:24:45Z)
• Long-Term Invariant Local Features via Implicit Cross-Domain Correspondences [79.21515035128832]
  We conduct a thorough analysis of the performance of current state-of-the-art feature extraction networks under various domain changes. We propose a novel data-centric method, Implicit Cross-Domain Correspondences (iCDC) iCDC represents the same environment with multiple Neural Radiance Fields, each fitting the scene under individual visual domains.
  arXiv  Detail & Related papers  (2023-11-06T18:53:01Z)
• Self-Supervised Temporal Analysis of Spatiotemporal Data [2.2720298829059966]
  There exists a correlation between geospatial activity temporal patterns and type of land use. A novel self-supervised approach is proposed to stratify landscape based on mobility activity time series. Experiments show that temporal embeddings are semantically meaningful representations of time series data and are effective across different tasks.
  arXiv  Detail & Related papers  (2023-04-25T20:34:38Z)
• Domain-incremental Cardiac Image Segmentation with Style-oriented Replay and Domain-sensitive Feature Whitening [67.6394526631557]
  M&Ms should incrementally learn from each incoming dataset and progressively update with improved functionality as time goes by. In medical scenarios, this is particularly challenging as accessing or storing past data is commonly not allowed due to data privacy. We propose a novel domain-incremental learning framework to recover past domain inputs first and then regularly replay them during model optimization.
  arXiv  Detail & Related papers  (2022-11-09T13:07:36Z)
• Towards Spatio-Temporal Aware Traffic Time Series Forecasting--Full Version [37.09531298150374]
  Traffic series forecasting is challenging due to complex time series patterns for the same time series patterns may vary across time, where, for example, there exist periods across a day showing stronger temporal correlations. Such-temporal models employ a shared parameter space irrespective of the time locations and the time periods and they assume that the temporal correlations are similar across locations and do not always hold across time which may not always be the case. We propose a framework that aims at turning ICD-temporal aware models to encode sub-temporal models.
  arXiv  Detail & Related papers  (2022-03-29T16:44:56Z)
• Batch Normalization Embeddings for Deep Domain Generalization [50.51405390150066]
  Domain generalization aims at training machine learning models to perform robustly across different and unseen domains. We show a significant increase in classification accuracy over current state-of-the-art techniques on popular domain generalization benchmarks.
  arXiv  Detail & Related papers  (2020-11-25T12:02:57Z)
• Connecting the Dots: Multivariate Time Series Forecasting with Graph Neural Networks [91.65637773358347]
  We propose a general graph neural network framework designed specifically for multivariate time series data. Our approach automatically extracts the uni-directed relations among variables through a graph learning module. Our proposed model outperforms the state-of-the-art baseline methods on 3 of 4 benchmark datasets.
  arXiv  Detail & Related papers  (2020-05-24T04:02:18Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.